Healthcare Analytics Adoption Model

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Why Healthcare Analytics?

Healthcare in the United States and other parts of the world has slowly been progressing through three waves of data management: data collection, data sharing, and data analytics. So far, the data collection and sharing waves, characterized by the urgent deployment of electronic health records and health information exchanges, have failed to significantly impact the quality and cost of healthcare. And despite the current hype about big data being the next “big” thing in other industries, the reality in healthcare is that we are just beginning to have the necessary analytics capabilities that enable system-wide quality improvement and cost reduction efforts. Healthcare analytics is the systematic use of data to create insights. The real promise of analytics lies in its ability to transform healthcare into a truly data-driven culture. And even beyond into analytics platforms such as the Health Catalyst Data Operating System (DOS).

Healthcare Analytics can be confusing, even overwhelming without a systematic framework for guiding your approach and priorities. The following framework, called the Healthcare Analytics Adoption Model, was developed by a cross-industry group of healthcare industry veterans as a guide to classifying groups of analytics capabilities, and provide a systematic sequencing to adopting analytics within health system organizations. A successful and sustainable analytics strategy requires building foundational elements of the model first in order to support the upper levels of the model in later years. View Contributors List

Purpose

The model borrows lessons learned from the HIMSS EMR Adoption Model, and describes an analogous approach for assessing the adoption of analytics in healthcare. The Healthcare Analytics Adoption Model provides:

A framework for evaluating the industry’s adoption of analytics
A roadmap for organizations to measure their own progress toward analytic adoption
A framework for evaluating vendor products

We believe that this Analytics Adoption Model will enable healthcare organizations to fully understand and leverage the capabilities of analytics and so achieve the ultimate goal that has eluded most provider organizations – that of improving the quality of care while lowering costs and enhancing clinician and patient satisfaction.

The Nine Levels of the Analytics Adoption Model

(click to reveal each level)

Level 9 – Direct-to-Patient Analytics & AI

Direct-to-patient analytics & AI

Analytics and AI are provided directly to patients which enables greater personal ownership and precision in their health decisions:

Direct-to-patient analytics and AI are used in a collaborative decision making environment between patients and healthcare providers.
Patients have the ability to port and analyze their complete healthcare data ecosystem, independent of healthcare providers.
Treatment and health maintenance protocols are enabled using AI-based digital twins– “Patients Like This” and “Patients Like Me” pattern recognition.

Level 8 – Personalized Medicine & Prescriptive Analytics

Contracting for & managing health

Personalized Medicine & Prescriptive Analytics:

Analytic motive expands to wellness management, physical and behavioral functional health, and mass customization of care.
Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support.
Prescriptive analytics are available at the point of care to improve patient specific outcomes based upon population outcomes.
Data content expands to include 7×24 biometrics data, genomic data and familial data.
The EDW is updated within a few minutes of changes in the source systems.

Level 7 – Clinical Risk Intervention & Predictive Analytics

Taking more financial risk & managing it proactively

Clinical Risk Intervention & Predictive Analytics:

Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models.
Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation and referrals.
Physicians, hospitals, employers, payers and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior).
Patients are flagged in registries who are unable or unwilling to participate in care protocols.
Data content expands to include home monitoring data, long term care facility data, and protocol-specific patient reported outcomes.
On average, the EDW is updated within one hour or less of source system changes.

Level 6 – Population Health Management & Suggestive Analytics

Taking financial risk and preparing your culture for the next levels of analytics

Population Health Management & Suggestive Analytics:

The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes.
At least 50% of acute care cases are managed under bundled payments.
Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care.
Data content expands to include bedside devices, home monitoring data, external pharmacy data, and detailed activity based costing.
Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives.
On average, the EDW is updated within one day of source system changes.
The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care.

Level 5 – Waste & Care Variability Reduction

Measuring & managing evidence based care

Clinical Effectiveness & Accountable Care:

Analytic motive is focused on measuring adherence to clinical best practices, minimizing waste, and reducing variability.
Data governance expands to support care management teams that are focused on improving the health of patient populations.
Population-based analytics are used to suggest improvements to individual patient care.
Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal workflows.
Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts.
EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries.
Data content expands to include insurance claims (if not already included) and HIE data feeds.
On average, the EDW is updated within one week of source system changes.

Level 4 – Automated External Reporting

Efficient, consistent production and agility

Automated External Reporting:

Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g. STS, NRMI, Vermont-Oxford).
Adherence to industry-standard vocabularies is required.
Clinical text data content is available for simple key word searches.
Centralized data governance exists for review and approval of externally released data.

Level 3 – Automated Internal Reporting

Efficient, consistent production

Automated Internal Reporting:

Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization.
Key performance indicators are easily accessible from the executive level to the front-line manager.
Corporate and business unit data analysts meet regularly to collaborate and steer the EDW.
Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above.

Level 2 – Standardized Vocabulary & Patient Registries

Relating and organizing the core data

Standardized Vocabulary & Patient Registries:

Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse.
Naming, definition, and data types are consistent with local standards.
Patient registries are defined solely on ICD billing data.
Data governance forms around the definition and evolution of patient registries and master data management.

Level 1 – Enterprise Data Warehouse

Foundation of data and technology

Enterprise Data Warehouse:

At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience.
Searchable metadata repository is available across the enterprise.
Data content includes insurance claims, if possible.
Data warehouse is updated within one month of source system changes.
Data governance is forming around the data quality of source systems.
The EDW reports organizationally to the CIO.

Level 0 – Fragmented Point Solutions

Inefficient, inconsistent versions of the truth

Fragmented Point Solutions:

Vendor-based and internally developed applications are used to address specific analytic needs as they arise.
The fragmented point solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another.
Overlapping data content leads to multiple versions of analytic truth.
Reports are labor intensive and inconsistent.
Data governance is non-existent.

Interested In a More In-Depth Explanation?

Dale Sanders, Dennis Protti and others have written a white paper, giving a more detailed explanation of each of the levels of the Healthcare Analytics Adoption Model. Click here to get a copy of the Healthcare Analytics Adoption Model White Paper or by clicking the link below. In addition, Dale recently presented a live webinar explaining the analytics adoption model. You can click here to see his webinar, and/or get copies of his slides, or even get a transcript of his webinar.

Take a Self-Assessment Survey to Find Out Where You Stand

Similar to the HIMSS EHR Adoption Model, there is a needed logical progression to become a systemic, analytics-driven organization. Health systems that aspire to higher-level results which attempt to tackle analytics in a scattered way are often frustrated by the lack of inadequate foundational platforms, tools, and skills that need to be mastered at the lower levels of the analytics adoption model. Based on our years of collective experience, we have designed a self-assessment survey to help you assess where your organization is consistently operating in each of the levels. Upon finishing the survey, you will be given a customized report either in HTML format or a PDF version that you can email to yourself, summarizing your status and a list of customized recommendations, based on your input. Click here to take the self-assessment survey.

Healthcare Analytics Is Evolving

The Health Catalyst Data Operating System (DOS™) is a breakthrough engineering approach that combines the features of data warehousing, clinical data repositories, and health information exchanges in a single, common-sense technology platform. Read more about DOS, our response to a simple truth: The future of healthcare will be centered around the broad and more effective use of data.

Healthcare Analytics Adoption Model

Why Healthcare Analytics?

Purpose

The Nine Levels of the Analytics Adoption Model